Teaching
Neural Data Science
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Teaching

Winter term 2024/2025

Current Topics in Deep Learning (Foundation Models: From Deep Learning to Neuroscience)

Seminar where recent deep learning papers are presented and discussed.

Alexander Ecker, Michaela Vystrčilová, Felix Benjamin Müller
Lecture Graph Machine Learning

Introduction to Graph Machine Learning

Martin Ritzert, Alexander Ecker, and Felix Müller

Summer term 2024

Deep Learning for Image Synthesis

Practical course on applying deep learning for image generation.

Alexander Ecker and Timo Lüddecke

Bachelor’s and Master’s theses

General requirements

If you’re interested in joining our lab for a thesis, have a look at this document describing how we work and what we expect from you.

We expect prospective students to have substantial knowledge in machine learning, its mathematical foundations and Python programming. We therefore expect students interested in doing their thesis in our lab to take our courses on Machine Learning and Deep Learning for Computer Vision unless they have acquired equivalent knowledge otherwise. For Bachelor’s students, we also recommend the Practical Course Data Science.

Further recommended courses are:

  • M.Inf.2241: Current Topics in Machine Learning (seminar)
  • M.Inf.2541: Current Topics in Computational Neuroscience (seminar)
  • M.Inf.2242: Journal Club Machine Learning and Computational Neuroscience (can be done in parallel to master’s thesis)
  • M.Inf.2201: Probabilistic Machine Learning (by Fabian Sinz)
  • B.Inf.1240: Visualization (by Bernahrd Schmitzer)

Please note, our thesis supervision capacity is limited and we receive more thesis inquiries than we are able supervise. Therefore, we have to select candidates. If you are interested, please write an email with the subject “Master’s thesis” or “Bachelor’s thesis” containing one to three sentences about what you would like to work on and your study record to the supervisor stated below.

We will get back to you within a few days. Otherwise, do not hesitate to remind us :).

Thesis offers

2D Clustering with Computer Vision
Using learned models for instance segmentation to cluster 2D data
Supervisor: Martin Ritzert
Automatic Detection, Segmentation and Classification of Lemur Vocalizations
Supervisor: Dr. Kaja Wierucka (external)
Biologically inspired CNN for ganglion cell response prediction
Implement a Convolutional Neural Network constraint by insights from the retinal circuitry to predict the responses of retinal ganglion cells.
Supervisor: Michaela Vystrčilová
Design Optimization for Acoustics
Using Neural Networks to predict the frequency responses on beading patterns.
Supervisor: Jan van Delden
Embeddings for neurons function and how they relate to morphology and cell types
Improve functional neuronal clustering
Supervisor: Polina Turishcheva
Implicit_learning_for_neuronal_prepresentation
Improve functional neuronal clustering
Supervisor: Polina Turishcheva
Mamba-inspired model for neuron-to-neuron architecture
Improve functional neuronal clustering
Supervisor: Polina Turishcheva
Model Neurons Interactions in time and between each other
Adjust readouts for neuroscience vision models to consider time and neurons interactions
Supervisor: Polina Turishcheva
Perspective module to module eye focus for mouse visual cortex
Adjust the module from the foundational model and try to make it sharable between animals
Supervisor: Polina Turishcheva
Self-Supervised Pretraining for Training Robust Monkey Detection and Tracking Models
Leveraging self-supervised pretraining to improve the robustness of monkey detection and tracking models in diverse environmental conditions.
Supervisor: Felix Müller
Solving Citation Networks with Large Language Models
By focussing on the abstract, LLMs should be able to effectively solve Cora and other citation datasets
Supervisor: Martin Ritzert
Tranformers as predictive models of the retina
Design and implement a transformer network to beat our state-of-the-art CNNs on retinal ganglion cell prediction tasks.
Supervisor: Michaela Vystrčilová
Treewidth-Based Positional Encodings
Generalizing a positional encoding for trees to general graphs using the notion of treewidth
Supervisor: Martin Ritzert
Neural Data Science Group
Institute of Computer Science
University of Goettingen